Somatic mutations in human cells have a highly heterogeneous genomic distribution, with increased burden in late-replication time (RT), heterochromatic domains of chromosomes. This regional mutation density (RMD) landscape is known to vary between cancer types, in association with tissue-specific RT or chromatin organization. Here, we hypothesized that regional mutation rates additionally vary between individual tumors in a manner independent of cell type, and that recurrent alterations in DNA replication programs and/or chromatin organization may underlie this. Here, we identified various RMD signatures that describe a global genome-wide mutation redistribution across many megabase-sized domains in >4000 tumors. We identified two novel global RMD signatures of somatic mutation landscapes that were universally observed across various cancer types. First, we identified a mutation rate redistribution preferentially affecting facultative heterochromatin, Polycomb-marked domains, and enriched in subtelomeric regions. This RMD signature strongly reflects regional plasticity in DNA replication time and in heterochromatin domains observed across tumors and cultured cells, which was linked with a stem-like phenotype and a higher expression of cell cycle genes. Consistently, occurrence of this global mutation pattern in cancers is associated with altered cell cycle control via loss of activity of the RB1 tumor suppressor gene. Second, we identified another independant global RMD signature associated with loss-of-function of the TP53 pathway, mainly affecting the redistribution of mutation rates away from late RT regions. The local mutation supply towards 26%-75% cancer driver genes is altered in the tumors affected by the global RMD signatures detected herein, including additionally a known pattern of a general loss of mutation rate heterogeneity due to DNA repair failures that we quantify. Our study highlights that somatic mutation rates at the domain scale are variable across tumors in a manner associated with loss of cell cycle control via RB1 or TP53, which may trigger the local remodeling of chromatin state and the RT program in cancers.

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Gene editing is revolutionizing bioscience and therapy. Our lab has developed a gene writing tool combining the precision of CRISPR and gene transfer capacity of a transposase (Pallarès-Masmitjà et al, Nat Com 2021). We are planning to develop improved gene writers for safer and more efficient therapies with aid of the B-bio (new retrovirus-based directed evolution platform developed by our lab (Ivancic et al, in preparation). A key part for the success of this massively genotype testing platform is library design and analysis using AI. Large language models are being very powerful to represent protein language and will be used to fuel genotypes and accelerate evolution. Coupling this biological hardware with AI is generating a fascinating synergy for enhancing the evolutionary rate of new CRISPR based editors and writers.

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Variation, i.e. the molecular and phenotypic changes caused by random mutations, is a crucial component of evolutionary processes. One biologically relevant example for which variation can be modelled computationally is the RNA genotype-phenotype (GP) map. This GP map links RNA sequences to their folded secondary structures and thus allows us to identify structural changes after sequence mutations. In this talk, I will describe some recent progress on the biophysics of this GP map, show what the properties of this GP map mean for evolutionary processes, and discuss what we can learn from this GP map for evolutionary processes beyond RNA

Big Data analytical techniques and AI have the potential to transform drug discovery, as they are reshaping other areas of science and technology, but we need to blend biology and chemistry in a format that is amenable for modern machine learning. In this talk, I will present the Chemical Checker (CC), a resource that provides processed, harmonized and integrated bioactivity data on small molecules. The CC divides data into five levels of increasing complexity, ranging from the chemical properties of compounds to their clinical outcomes. In between, it considers targets, off-targets, perturbed biological networks and several cell-based assays such as gene expression, growth inhibition and morphological profiles. I will also present the Bioteque, a resource of unprecedented size and scope that contains pre-calculated biomedical embeddings around 11 biological entities (e.g. genes, cells, tissues, disease, etc), derived from a gigantic knowledge graph, so that each entity can be described considering different contexts (e.g. interactions, expression, etc). With small molecule and biological bioactivity descriptors in hand, we now face a new scenario for chemical and biological entities where they both are translated into a common numerical format. In this computational framework, complex connections between entities can be unveiled by means of simple arithmetic operations. Indeed, we demonstrate and experimentally validate that these descriptors can be used to reverse and mimic biological signatures of disease models and genetic perturbations in vitro and in vivo, options that are otherwise impossible using chemical information alone.

References
Duran-Frigola et al. Extending the small molecule similarity principle to all levels of biology with the Chemical Checker. 2020. Nat Biotechnol. 38: 1087-1096.

Bertoni et al. Bioactivity descriptors for uncharacterized chemical compounds. 2021. Nat Commun. 12: 3932.

Pauls et al. Identification and drug-induced reversion of molecular signatures of Alzheimer's disease onset and progression in AppNL-G-F, AppNL-F, and 3xTg-AD mouse models. 2021. Genome Med. 13:168.

Fernández-Torras et al. Connecting chemistry and biology through molecular descriptors. 2022. Curr Opin Chem Biol, 66: 102090.

Fernández-Torras et al. Integrating and formatting biomedical data in the Bioteque, a comprehensive repository of pre-calculated knowledge graph embeddings. 2022. Nat Commun. 13: 5304.

 

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The cis-regulatory code that instructs gene regulation during development, also known as the genome’s second code, is a fundamentally unresolved problem. Recent progress has provided proof-of-principle evidence that this complex cis-regulatory code can be learned with neural networks. The new approach is fundamentally different from traditional methods in that the sequence rules are learned inside a black box directly from genomic sequences through their ability to better predict a specific genomics data set. This dramatically improves the predictive performance, but requires rigorous approaches for extracting, understanding and validating the learned sequence rules to make sure that they represent biology. I will describe how we use this approach using mouse or Drosophila development as model systems and uncover sequence rules that we can validate with experiments. The goal is to understand the underlying biophysical processes and constraints and to create a general model of how the cis-regulatory code is read out by transcription factors. We strive to use this knowledge to create more powerful deep learning models that learn cis-regulatory sequence rules more broadly across cell types.

Vascular regression is crucial for limb development and pattern formation. We investigated this
phenomenon using experimental modeling and mathematical approaches. In our in vitro model,
we observed that Sox9 expression affects the behavior of endothelial cells, mimicking in vivo
patterns. To further understand vascular regression, we developed a hybrid mathematical
model that incorporates cell interactions, mechanics, and Sox9 pre-pattern formation. By
combining experimental data and modeling, we gained insights into vasculature network
formation and its role in organogenesis. Our interdisciplinary approach represents a first step in
unraveling the vascular regression and advancing our understanding of limb development.

Spontaneous pattern formation provides a physical basis for morphogenesis, as put forth by D’Arcy Thompson and Turing. In this talk, I will discuss ongoing work to understand collective invasion during metastasis using similar principles. We study as a model system patient-derived breast cancer organoids in collagen matrices developed by the Khalil group at Utrecht. We propose a hypothesis for a positive feedback loop driving collective invasion, which involves an interplay between mechanical and chemical activity of cells and the nonlinear mechanics of collagen. We examine the consequences of this hypotheses using mathematical and computational modeling. I will also present preliminary experiments testing the proposed mechanism.

Over the last fifty years, interdisciplinary approaches in biology have become popular with the emergence of fields such as systems biology and biophysics. Mathematical modelling and the use of quantitative approaches have been successful in shedding light into the emergent behavior of cells and tissues. However, despite great advances in molecular cell biology and genetics, we are still far from building predictive models describing the self-organization of living systems. One of the main drawbacks is the poor connection between microscopic and mesoscopic descriptions, a well-known problem in physics. In this talk, I will propose the use of techniques borrowed from soft matter physics to bridge scales in biological systems, thus unveiling how mesoscopic quantities depend on microscopic quantities such as kinetic parameters. In particular, I will focus on the role of solid-to-fluid transitions at the subcellular and supracellular scales and show how multiscale modelling will be key in the future to build predictive theories in biology.

Tissue structure emerges through the process of self-organization. We aim to build a bottom-up understanding of tissue self-organization by studying the biophysical properties of single cells and their interactions. We seek cross-cutting principles by working in organ systems as diverse as the mammary gland, intestine, pancreas and blood. Our long-term goal is to leverage a detailed understanding of tissue self-organization for applications in regenerative medicine, disease modeling, and therapeutics discovery. Current projects aim to elaborate statistical mechanical models of tissue structure, reveal active mechanical mechanisms of tissue patterning, and develop new tools to systematically measure and engineer the structure of tissues both in vitro and in vivo.